Msc Thesis Figures
Valentin Lucet
17/07/2020
Data prep
Cleaning and formatting
We start by preparing the data from the outputs of scripts 7.1 and 8.1. The results table shown as output is the key to read the figures.
# Results dir + mun shapefile
mun <- st_read("../data/mun/munic_SHP_clean.shp", quiet = TRUE)
# Classes
classes <- read_csv("../config/rcl_tables/land_use/recode_table.csv")
forest_classes <- read_csv("../config/rcl_tables/land_use/recode_table_forest.csv") %>%
rename(new_code = "ID", new_class = "Name")
classes_unique <- unique(classes[, c("new_code", "new_class")]) %>%
bind_rows(forest_classes)
# results key
sce_code_vec <-
as.vector(t(outer(c("BAU-", "R-", "CorPr-", "R-CorPr-", "R(T)-CorPr-"),
c("Hist", "Base", "RCP8"), paste0)))
sce_code_vec_run <-
rep(c("BAU", "R", "CorPr", "R-CorPr", "R(T)-CorPr"), each=3)
results_clean <- read_rds("../data/temp/stsim_run_results.RDS") %>%
dplyr::select(scenarioId, name) %>%
mutate(name = gsub(.$name, pattern = " \\(.+\\)", replacement = "")) %>%
mutate(sce = paste0("sce_", .$scenarioId)) %>%
mutate(chapter = c("none", rep("both", 3), rep("chap_1", 3), rep("chap_2", 9))) %>%
mutate(splitted = str_split(name, " \\| ")) %>%
mutate(climate = unlist(map(splitted, ~unlist(.x[2])))) %>%
mutate(run = unlist(map(splitted, ~unlist(.x[1])))) %>%
dplyr::select(-splitted) %>%
replace_na(list(climate = "none")) %>%
mutate(code = c("Control", sce_code_vec)) %>%
mutate(code_run = c("control", sce_code_vec_run))
# Final extracted datasets
df_final <- readRDS("../outputs/final/final_df_current_density.RDS") %>%
mutate(timestep = (timestep*10)+1990, source = "model")
df_final_origin <- readRDS("../outputs/final/final_df_origin_current_density.RDS") %>%
mutate(timestep = timestep*10+1990, source = "model")
# Stratum key
stratum_key <- read_csv("../config/stsim/SecondaryStratum.csv") %>%
mutate(ID = as.factor(ID)) %>%
rename(zone=ID, MUS_NM_MUN=Name) %>%
left_join(df_final, by="zone") %>%
filter(MUS_NM_MUN!="Not Monteregie")
# Sum all municipalities
df_summarised <- df_final %>%
group_by(sce, timestep, species, iteration) %>%
summarise(sum_cur = sum(current)) %>% ungroup %>%
mutate(source = "model")
df_origin_summarised <- df_final_origin %>%
group_by(timestep, species) %>%
summarise(sum_cur = sum(mean)) %>% ungroup %>%
mutate(sce = "sce_0", source = "observation")
# Joined dataset
joined <- full_join(df_summarised, df_origin_summarised,
by=c("sce", "source", "species", "timestep", "sum_cur")) %>%
left_join(results_clean, by = "sce") %>%
replace_na(list(climate = "none", run = "historic run")) %>%
# Make factors
mutate(sce = as.factor(sce), run = as.factor(run), sum_cur = 10*(sum_cur),
climate = factor(climate, levels = c("none", "historic",
"baseline", "RCP 8.5")),
run = factor(run, levels = c("historic run", "BAU run",
"BAU run + corrs protection", "BAU run + ref",
"BAU run + corrs protection + ref",
"BAU run + corrs protection + ref TARGETED")))
# Roc curves data
urb <- readRDS("../data/temp/fit_rs_outcome_rf_urb_2.RDS")
agex <- readRDS("../data/temp/fit_rs_outcome_rf_agex_2.RDS")
# Histogram data
hist_original <- read_csv("../outputs/final/final_values_output_original.csv")
hist_true <- read_csv("../outputs/final/final_values_output_TRUE.csv") %>%
mutate(sce = "TRUE")
histograms <- read_csv("../outputs/final/final_values_output.csv") %>%
bind_rows(hist_original) %>%
bind_rows(hist_true) %>%
left_join(results_clean, by="sce")
# SURF Data
surf <- read_csv("../surf/surf_output.csv") %>%
mutate(timestep = timestep*10+1990) %>%
left_join(results_clean, by=c("scenario"="scenarioId")) %>%
replace_na(list(climate = "none", run = "historic run")) %>%
# Make factors
mutate(sce = as.factor(sce), run = as.factor(run),
climate = factor(climate, levels = c("none", "historic",
"baseline", "RCP 8.5")),
run = factor(run, levels = c("historic run", "BAU run",
"BAU run + corrs protection", "BAU run + ref",
"BAU run + corrs protection + ref",
"BAU run + corrs protection + ref TARGETED")))
# Bar plot data
bar_data <- read_csv("../outputs/final/final_bar_plot_data.csv")
results_cleanFigures
Mean current flow change
Historic
the_width = 18
the_height = 15
the_dpi = 500
fig_1_historic <- joined %>%
filter(climate == "none") %>%
group_by(scenarioId, species, iteration) %>%
group_modify(~mutate(.x , baseline = subset(.x, timestep == 1990)$sum_cur,
the_diff = ((sum_cur-baseline)/baseline)*100 )) %>%
ggplot(aes(x=timestep, y=the_diff, col=source)) +
scale_color_manual(values=c('#d8b365','#5ab4ac'),
labels = c("Model", "Observation")) +
geom_smooth(aes(group = sce), method = "glm", se=FALSE) +
scale_linetype_manual(values = c(1,3,2,5))+
geom_point(aes(group = sce)) +
facet_wrap(~species, scales = "fixed") +
labs(y = "Current flow (% Change)",
x = "Year",
col = "Source") +
NULL
fig_1_historic
Connectivity change for species through time, 1990-2010
ggsave(fig_1_historic,
filename = "../thesis/figures/connectivity_decrease_x5species_historic.png",
width = the_width, height = the_height, dpi = the_dpi)Chap 1
Line graph
colors_sce <-
data.frame(sce = c("Hist", "Baseline", "RCP85"),
color = c("#8DA0CB", "#FC4E2A", "#800026"), stringsAsFactors = F)
fig_1_static_chap_1 <- joined %>%
filter(climate != "none", chapter %in% c("none", "both", "chap_1")) %>%
group_by(scenarioId, species, iteration) %>%
group_modify(~mutate(.x , baseline = subset(.x, timestep == 2010)$sum_cur,
the_diff = ((sum_cur-baseline)/baseline)*100 )) %>%
ggplot(aes(x=timestep, y=the_diff, col=climate)) +
scale_color_manual(values = colors_sce$color,
labels = c("Historic", "Baseline", "RCP 8.5")) +
geom_smooth(aes(group = sce, linetype=code_run), alpha=0.5, method="loess") +
#geom_point(aes(group = sce)) +
scale_linetype_manual(values=c("solid", "twodash")) +
facet_wrap(~species, scales = "fixed") +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
#geom_vline(xintercept = 2020, linetype = "dashed", color = "darkGreen", alpha = 0.5) +
labs(y = "Current flow (% Change)",
x = "Year",
col = "Climate",
linetype = "Run") +
guides(col = guide_legend(ncol = 2, nrow=2),
linetype = guide_legend(nrow=1, override.aes = list(colour = 'black'))) +
theme(legend.position = c(0.85, 0.15))+
NULL
fig_1_static_chap_1
Connectivity change for species through time, 2010-2100
ggsave(fig_1_static_chap_1,
filename = "../thesis/figures/connectivity_decrease_x5species_chap1.png",
width = the_width, height = the_height, dpi = the_dpi)Radar Graph
# Default size
gridline.label.offset = 4
legend.text.size = 20
group.line.width = 0.9
grid.label.size = 10
background.circle.colour = "#ffffff"
group.point.size = 5
axis.label.size = 8
group.colours = RColorBrewer::brewer.pal(name = "Paired",n=10)[c(2,4,6,8,10)]
radar_data_chap1 <- joined %>%
filter(climate != "none", chapter %in% c("none", "both", "chap_1")) %>%
group_by(timestep, species, code) %>%
summarise(sum_cur = mean(sum_cur)) %>% ungroup %>%
pivot_wider(names_from = timestep, values_from = sum_cur) %>%
mutate(diff = ((`2100`-`2010`)/`2010`)*100) %>%
select(-c(`2010`:`2100`)) %>%
pivot_wider(names_from = code, values_from = diff) %>%
rename(group = species)
radar_chap1 <-
ggradar(radar_data_chap1, centre.y = -20, legend.position = "right",
grid.min = -20, grid.max = 3, grid.mid = 0,
values.radar = c("-20 %", "0 %", ""),
group.colours = group.colours,
gridline.label.offset = gridline.label.offset,
legend.text.size = legend.text.size,
group.line.width = group.line.width,
grid.label.size = grid.label.size,
background.circle.colour = background.circle.colour,
group.point.size = group.point.size,
axis.label.size = axis.label.size)
radar_chap1
ggsave("../thesis/figures/radar_ggradar_chap1.png", radar_chap1,
width = the_width, height = the_height, dpi = the_dpi)Chap 2
fig_1_static_chap_2 <- joined %>%
filter(climate != "none", chapter %in% c("none", "both", "chap_2")) %>%
group_by(scenarioId, species, iteration) %>%
group_modify(~mutate(.x , baseline = subset(.x, timestep == 2010)$sum_cur,
the_diff = ((sum_cur-baseline)/baseline)*100 )) %>%
ggplot(aes(x=timestep, y=the_diff, col=climate)) +
scale_color_manual(values = colors_sce$color,
labels = c("Historic", "Baseline", "RCP 8.5")) +
geom_smooth(aes(group = sce, linetype=code_run), alpha=0.5, method="loess") +
scale_linetype_manual(values=c("solid", "dashed", "longdash", "dotted")) +
facet_wrap(~species, scales = "fixed") +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
geom_vline(xintercept = 2020, linetype = "dashed", color = "darkGreen", alpha = 0.5) +
labs(y = "Current flow (% Change)",
x = "Year",
col = "Climate",
linetype = "Run") +
guides(col = guide_legend(ncol = 2, nrow=2),
linetype = guide_legend(nrow = 2, ncol=2,
override.aes = list(colour = 'black'))) +
theme(legend.position = c(0.85, 0.15))+
NULL
fig_1_static_chap_2
Connectivity change for species through time, 2010-2100
ggsave(fig_1_static_chap_2,
filename = "../thesis/figures/connectivity_decrease_x5species_chap2.png",
width = the_width, height = the_height, dpi = the_dpi)Radar Graph
radar_data_chap2 <- joined %>%
filter(climate != "none", chapter %in% c("none", "both","chap_2")) %>%
group_by(timestep, species, code) %>%
summarise(sum_cur = mean(sum_cur)) %>% ungroup %>%
pivot_wider(names_from = timestep, values_from = sum_cur) %>%
mutate(diff = ((`2100`-`2010`)/`2010`)*100) %>%
select(-c(`2010`:`2100`)) %>%
pivot_wider(names_from = code, values_from = diff) %>%
rename(group = species)
radar_chap2 <-
ggradar(radar_data_chap2, centre.y = -20, legend.position = "right",
grid.min = -20, grid.max = 3, grid.mid = 0,
values.radar = c("-20 %", "0 %", ""),
group.colours = group.colours,
gridline.label.offset = gridline.label.offset,
legend.text.size = legend.text.size,
group.line.width = group.line.width,
grid.label.size = grid.label.size,
background.circle.colour = background.circle.colour,
group.point.size = group.point.size,
axis.label.size = axis.label.size-2)
radar_chap2
ggsave("../thesis/figures/radar_ggradar_chap2.png", radar_chap2,
width = the_width, height = the_height, dpi = the_dpi)Both
Radar Graph
radar_data_all <- joined %>%
filter(climate != "none") %>%
group_by(timestep, species, code) %>%
summarise(sum_cur = mean(sum_cur)) %>% ungroup %>%
pivot_wider(names_from = timestep, values_from = sum_cur) %>%
mutate(diff = ((`2100`-`2010`)/`2010`)*100) %>%
select(-c(`2010`:`2100`)) %>%
pivot_wider(names_from = code, values_from = diff) %>%
rename(group = species)
radar_all <-
ggradar(radar_data_all, centre.y = -20, legend.position = "right",
grid.min = -20, grid.max = 3, grid.mid = 0,
values.radar = c("-20 %", "0 %", ""),
group.colours = group.colours,
gridline.label.offset = gridline.label.offset,
legend.text.size = legend.text.size,
group.line.width = group.line.width,
grid.label.size = grid.label.size,
background.circle.colour = background.circle.colour,
group.point.size = group.point.size,
axis.label.size = axis.label.size-2)
radar_all
ggsave("../thesis/figures/radar_ggradar_both.png", radar_all,
width = the_width, height = the_height, dpi = the_dpi)ROC Curves
ROC curves from fitting both models, plotting with patchwork package.
p1 <- bind_rows(urb, .id = "fold") %>%
mutate(Fold = factor(fold, levels = as.character(1:10))) %>%
group_by(Fold) %>%
roc_curve(.pred, truth = outcome_fact) %>%
autoplot(add=T) +
ggtitle(paste("Urbanisation")) +
annotate(x = 0.75, y = 0.25, geom="label", size = 3,
label = as.character(paste("Av AUC = 0.938 +/- 0.002"))) +
theme(legend.position = "none") +
NULL
p2 <- bind_rows(agex, .id = "fold") %>%
mutate(Fold = factor(fold, levels = as.character(1:10))) %>%
group_by(Fold) %>%
roc_curve(.pred, truth = outcome_fact) %>%
autoplot(add=T) +
ggtitle(paste("Agricultural Expansion")) +
annotate(x = 0.75, y = 0.25, geom="label", size = 3,
label = as.character(paste("Av AUC = 0.929 +/- 0.002"))) +
NULL
full = p1 + p2
full
ggsave("../thesis/figures/double_roc_resample.png", full)Histograms
Original
bi_colors <- c("#67a9cf", "#ef8a62")
hist_plot_origin <- histograms %>%
filter(sce %in% c("TRUE", "sce_37")) %>%
filter(ts == 2) %>%
mutate(ts = ts*10+1990) %>%
mutate(ts = as.factor(ts)) %>%
ggplot(aes(x=bins, y=n, fill=sce, colour =sce)) +
geom_density(stat='identity', show.legend=T, alpha=0.3)+
scale_fill_manual(values = bi_colors, name="Source", labels=c("Model", "Observation")) +
scale_color_manual(values = bi_colors, name="Source", labels=c("Model", "Observation")) +
facet_wrap(~species) +
labs(x = "Flow intensity distribution (log)",
y = "") +
theme(strip.text.y = element_text(angle=360, size=10, hjust = 0),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y.left = element_blank(),
axis.ticks.y.left = element_blank()) +
NULL
hist_plot_origin
ggsave("../thesis/figures/original_hists.png", hist_plot_origin,
width = the_width, height = the_height, dpi = the_dpi)Chap 1
bi_colors <- c("#67a9cf", "#ef8a62")
hist_plot_1 <- histograms %>%
mutate(ts = as.factor(ts)) %>%
filter(chapter %in% c('both', 'chap_1')) %>%
ggplot(aes(x=bins, y=n, group=ts)) +
geom_density(stat='identity', show.legend=T, aes(fill=factor(ts), color=factor(ts)), alpha=0.3)+
facet_grid(code~species) +
scale_fill_manual(values = bi_colors,
labels = c("2010", "2100")) +
scale_color_manual(values = bi_colors,
labels = c("2010", "2100")) +
labs(x = "Flow intensity distribution (log)",
y = "")+
theme(strip.text.y = element_text(angle=360, size=10, hjust = 0),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y.left = element_blank(),
axis.ticks.y.left = element_blank(),
legend.position = c(0.95, 0.96)) +
labs(color="Timestep", fill="Timestep") +
NULL
hist_plot_1
ggsave("../thesis/figures/hist_chap1.png", hist_plot_1, width = 18, height = 15)Chap 2
hist_plot_2 <- histograms %>%
mutate(ts = as.factor(ts)) %>%
filter(chapter %in% c('chap_2')) %>%
ggplot(aes(x=bins, y=n, group=ts)) +
geom_density(stat='identity', aes(fill=factor(ts), colour=factor(ts)), alpha=0.3)+
facet_grid(code~species) +
scale_fill_manual(values = bi_colors,
labels = c("2010", "2100")) +
scale_color_manual(values = bi_colors,
labels = c("2010", "2100")) +
labs(x = "Flow intensity distribution (log)",
y = "") +
theme(strip.text.y = element_text(angle=360, size=10, hjust = 0),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y.left = element_blank(),
axis.ticks.y.left = element_blank(),
legend.position = c(0.95, 0.96)) +
labs(color="Timestep", fill="Timestep") +
NULL
hist_plot_2
ggsave("../thesis/figures/hist_chap2.png", hist_plot_2, width = 18, height = 20)SURF Analysis
Chap1
Linear Graph
surf_1 <- surf %>%
mutate(scenario = as.factor(scenario)) %>%
filter(climate != "none", chapter %in% c("none", "both", "chap_1")) %>%
filter(climate != "none") %>%
group_by(scenario, species, iter) %>%
group_modify(~mutate(.x , baseline = subset(.x, timestep == 2010)$kp_nb,
the_diff = ((kp_nb-baseline)/baseline)*100 )) %>% ungroup %>%
ggplot(aes(x = timestep, y = the_diff, color=climate)) +
scale_linetype_manual(values=c("solid", "twodash")) +
geom_smooth(aes(group = scenario, linetype=code_run), alpha=0.2, method="loess")+
#geom_point(aes(group=scenario), alpha= 0.05, size = 0.5)+
facet_wrap(~species, scales = "fixed")+
scale_color_manual(values = colors_sce$color,
labels = c("Historic", "Baseline", "RCP 8.5")) +
labs(y = "Change in nb of Keypoints detected (%)",
x = "Year",
col = "Climate",
linetype = "Run") +
guides(col = guide_legend(ncol = 2, nrow=2),
linetype = guide_legend(nrow=1, override.aes = list(colour = 'black'))) +
NULL
surf_1
ggsave("../thesis/figures/surf_chap1.png", surf_1,
width = the_width, height = the_height, dpi = the_dpi)Radar Graph
surf_radar_data_1 <- surf %>%
filter(climate != "none", chapter %in% c("none", "both","chap_1")) %>%
filter(sce != "sce_0", name != "historic run") %>%
group_by(timestep, species, code) %>%
summarise(kp_nb = mean(kp_nb)) %>% ungroup %>%
pivot_wider(names_from = timestep, values_from = kp_nb) %>%
mutate(diff = ((`2100`-`2010`)/`2010`)*100) %>%
select(-c(`2010`:`2100`)) %>%
pivot_wider(names_from = code, values_from = diff) %>%
rename(group = species)
surf_radar_1 <-
ggradar(surf_radar_data_1, centre.y = -60, legend.position = "right",
grid.min = -60, grid.max = 5, grid.mid = 0,
values.radar = c("-60 %", "0 %", ""),
group.colours = group.colours,
gridline.label.offset = 20,
legend.text.size = legend.text.size,
group.line.width = group.line.width,
grid.label.size = grid.label.size,
background.circle.colour = background.circle.colour,
group.point.size = group.point.size,
axis.label.size = axis.label.size)
surf_radar_1
ggsave("../thesis/figures/surf_radar_chap1.png", surf_radar_1,
width = the_width, height = the_height, dpi = the_dpi)Chap 2
Linear Graph
surf_2 <- surf %>%
mutate(scenario = as.factor(scenario)) %>%
filter(climate != "none", chapter %in% c("none", "both", "chap_2")) %>%
filter(climate != "none") %>%
group_by(scenario, species, iter) %>%
group_modify(~mutate(.x , baseline = subset(.x, timestep == 2010)$kp_nb,
the_diff = ((kp_nb-baseline)/baseline)*100 )) %>% ungroup %>%
ggplot(aes(x = timestep, y = the_diff, color=climate)) +
geom_smooth(aes(group = scenario, linetype=code_run), alpha=0.2, method="loess")+
#geom_point(aes(group=scenario), alpha= 0.05, size = 0.5)+
scale_linetype_manual(values=c("solid", "dashed", "longdash", "dotted")) +
facet_wrap(~species, scales = "fixed")+
scale_color_manual(values = colors_sce$color,
labels = c("Historic", "Baseline", "RCP 8.5")) +
labs(y = "Change in nb of Keypoints detected (%)",
x = "Year",
col = "Climate",
linetype = "Run") +
guides(col = guide_legend(ncol = 2, nrow=2),
linetype = guide_legend(nrow=2, ncol=2,
override.aes = list(colour = 'black'))) +
NULL
surf_2
ggsave("../thesis/figures/surf_chap2.png", surf_2,
width = the_width, height = the_height, dpi = the_dpi)Radar Graph
surf_radar_data_2 <- surf %>%
filter(climate != "none", chapter %in% c("none", "both","chap_2")) %>%
filter(sce != "sce_0", name != "historic run") %>%
group_by(timestep, species, code) %>%
summarise(kp_nb = mean(kp_nb)) %>% ungroup %>%
pivot_wider(names_from = timestep, values_from = kp_nb) %>%
mutate(diff = ((`2100`-`2010`)/`2010`)*100) %>%
select(-c(`2010`:`2100`)) %>%
pivot_wider(names_from = code, values_from = diff) %>%
rename(group = species)
surf_radar_2 <-
ggradar(surf_radar_data_2, centre.y = -60, legend.position = "right",
grid.min = -60, grid.max = 5, grid.mid = 0,
values.radar = c("-60 %", "0 %", ""),
group.colours = group.colours,
gridline.label.offset = 22,
legend.text.size = legend.text.size,
group.line.width = group.line.width,
grid.label.size = grid.label.size,
background.circle.colour = background.circle.colour,
group.point.size = group.point.size,
axis.label.size = axis.label.size-2)
surf_radar_2
ggsave("../thesis/figures/surf_radar_chap2.png", surf_radar_2,
width = the_width, height = the_height, dpi = the_dpi)Maps
Land use maps
source('~/Documents/Master/Thesis/land_con_monteregie/scripts/functions/draw_scenario.R')
for (scenario in c(39, 42, 45, 48, 51)){
the_plot <- draw_scenario(scenario, mxp = mxp)
the_name <- paste0("scenario_", as.character(scenario),"_compare.png")
ggsave(plot = the_plot,
filename = paste0("../thesis/figures/",the_name), width = 20, height = 10, dpi = the_dpi)
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## )## Parsed with column specification:
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## )## Parsed with column specification:
## cols(
## ID = col_double(),
## Name = col_character()
## )## Parsed with column specification:
## cols(
## source = col_character(),
## name = col_character(),
## class = col_character(),
## subclass = col_logical(),
## old_code = col_double(),
## new_code = col_double(),
## new_class = col_character()
## )## Parsed with column specification:
## cols(
## ID = col_double(),
## Name = col_character()
## )Bar charts
Sce 39
final_df_39 <- bar_data %>%
filter(scenario == 39)
bar_plot_39 <- final_df_39 %>%
#filter(value > 10) %>%
mutate(value = as.factor(value), timestep = as.factor(timestep)) %>%
ggplot(aes(fill=timestep, y=area, x=new_class)) +
geom_bar(position="dodge", stat="identity") +
#geom_errorbar(aes(ymin=mean_count-sd_count, ymax=mean_count+sd_count), width=.2,
# position=position_dodge(.9)) +
scale_fill_manual(values = bi_colors) +
theme(legend.position = c(0.85, 0.7)) +
labs(x = "Land use Class",
y = "Mean Area (hectare)",
fill = "Year") +
NULL
bar_plot_39
#ggsave("../thesis/figures/bar_BAU.png", bar_plot_39, width = 15, height = 10)Sce 42
# draw_scenario(42)
final_df_42 <- bar_data %>%
filter(scenario == 42)
bar_plot_42 <- final_df_42 %>%
#filter(value > 10) %>%
mutate(value = as.factor(value), timestep = as.factor(timestep)) %>%
ggplot(aes(fill=timestep, y=area, x=new_class)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_manual(values = bi_colors) +
#geom_errorbar(aes(ymin=mean_count-sd_count, ymax=mean_count+sd_count), width=.2,
# position=position_dodge(.9)) +
theme(legend.position = c(0.85, 0.7)) +
labs(x = "Land use Class",
y = "Mean Area (hectare)",
fill = "Year") +
NULL
bar_plot_42
#ggsave("../thesis/figures/bar_Ref.png", bar_plot_42, width = 15, height = 10)Both
final_df_39 <- final_df_39 %>%
mutate(sce = "BAU") %>%
mutate(type = ifelse(value <10, "non_forest", "forest"))
final_df_42 <- final_df_42 %>%
mutate(sce = "Reforestation") %>%
mutate(type = ifelse(value <10, "non_forest", "forest"))
final_all <- bind_rows(final_df_39, final_df_42)
forest <- final_all %>%
filter(type == "forest") %>%
mutate(value = as.factor(value), timestep = as.factor(timestep)) %>%
ggplot(aes(fill=timestep, y=area, x=new_class)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_manual(values = bi_colors) +
theme(legend.position = "none") +
facet_grid(~sce) +
labs(x = "Land use Class",
y = "Mean Area (hectare)",
fill = "Year") +
NULL
non_forest <- final_all %>%
filter(type != "forest") %>%
mutate(value = as.factor(value), timestep = as.factor(timestep)) %>%
ggplot(aes(fill=timestep, y=area, x=new_class)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_manual(values = bi_colors) +
theme(legend.position = "right") +
facet_grid(~sce) +
labs(x = "Land use Class",
y = "Mean Area (hectare)",
fill = "Year") +
NULL
assembled <- wrap_plots(forest, non_forest, ncol=1, nrow=2)
assembled
ggsave("../thesis/figures/bar_both.png", assembled,
width = the_width, height = 20, dpi = the_dpi)